AUTOLOGOUS RETINAL GRAFT SURGERY FOR REFRACTORY MACULAR HOLES WITHOUT POSTOPERATIVE HEAD POSITIONING
Bibliographic record
Abstract
PURPOSE: To evaluate the efficacy of autologous retinal graft (ARG) surgery using a novel technique of Viscoat as a graft adherent and stabilizer for large, refractory macular holes (MHs) without postoperative face-down positioning. METHODS: This retrospective interventional case series included 13 patients with refractory MHs who underwent ARG surgery. The surgical technique involved retinal graft placement stabilized with Viscoat, without postoperative positioning. Preoperative, 6 months and 12 months postoperative outcomes, including MH closure rates and visual acuity (VA) were analyzed. RESULTS: Pre-op mean MH size was 821.69 ± 180.65 µm (range: 563-1200 µm). Anatomical closure was achieved in 76.9% (10/13) of cases. Median VA improved from 1.7 logMAR (20/1000) preoperatively to 1.3 logMAR (20/400) at 6 months and at 12 months postoperatively, although this change was not statistically significant (p = 0.106 and p = 0.311 respectively). No major complications were reported. Larger MH size and chronicity might limit functional improvement despite successful closure. CONCLUSION: This is the first study to demonstrate that ARG surgery with Viscoat can achieve high closure rates without postoperative head positioning. The technique offers a patient-friendly alternative for refractory MH management, reducing postoperative burden while maintaining promising anatomical outcomes. Further studies with larger cohorts are warranted to validate these findings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".